Peter Swain is intrigued by how cells make decisions, but one decision in his life was easy – to come to Scotland as a SULSA professor, based in the Centre for Systems Biology at the University of Edinburgh……
Systems biologists like Peter Swain sometimes live in a world of extremes. One moment, he is trying to think like a bacterium, then the next he is trying to work out the ‘grander design’ which makes life possible and ‘programmes’ evolution.
Like many other systems biologists, Swain also uses a language that traditional biologists would find hard to swallow, talking about stochasticity and “deviant effects” in the same sentence as slime mould. For example, he writes: “We argue that the cellular viewpoint can only be probabilistic and that cellular decision-making strategies occur at three levels, described by ideas from statistical inference, decision theory, and evolutionary game theory.”
But despite this different language, ultimately Swain and his colleagues are concerned with exactly the same biological issues and have the same goals in mind – to improve the quality of life by understanding how organisms grow and develop, and how life evolves, in order to develop drugs to save and sustain human life.
In the process, Swain’s career is heading inexorably in the direction of a new science called synthetic biology, which fits in nicely with the future agenda of SULSA (the Scottish Universities Life Sciences Alliance), which he joined at the end of last year. If you tried to pigeonhole Swain, however, by describing him as a systems biologist or a mathematician (he gained his PhD in mathematics at Imperial College in London), he would sidestep the issue by saying he simply “does science.”
Swain’s journey to Edinburgh was via Northern Ireland, Trinity College in Cambridge, then Imperial College, a Max Planck Institute in Berlin, the department of physics at Tel Aviv University, the Rockefeller University in New York and the department of physiology at McGill University in Canada. Along the way, he gravitated steadily from applied mathematics and the physics of membranes to the new discipline of systems biology, when his early fascination with biology found its expression in the new computational methods that scientists started applying to biology in the late 1990s, at the time of the Human Genome Project. According to Swain, this period also saw a shift in the scientific community and the technology at its disposal, including new tools such as fluorescent protein imaging and developments in microscopy, as well as information technology. As a result, there were mountains of data to process, as biologists struggled to understand complex subjects such as how cells make decisions and communicate, and how genes and proteins interact.
For Swain, a major issue was stochasticity – the random behaviour of cells. Scientists had talked about stochasticity for decades, but Swain set out to prove that the theories were right, starting with the observation that in any biochemical network, stochastic effects become more dramatic when the number of copies of each protein is smaller.
After building mathematical models, the next step was to carry out experiments to quantify the sometimes random behaviour of proteins, then repeat the experiments and process all the data, looking at “noise” in the fluctuating levels of protein produced when genes are switched on and off. According to Swain, characterising such stochasticity, how cells interact and communicate with each other, and the fluctuating environment where the cells live, could lead to an understanding of how cells evolve and adapt to their living conditions by making decisions.
As Swain writes on the home page of the Swain Lab: “We study how cells make decisions. Gathering and processing information is fundamental to life. In all cells, this ability is conferred by biochemical networks, collections of genes and proteins that interact with each other and the extracellular environment. Information is detected by proteins at the cell membrane, processed by biochemical networks in the cytosol and nucleus, and then used to decide an appropriate cellular response. Such cellular decision making is at the core of synthetic biology and its failure causes disease: whether it is a hijacking of the signalling network by a viral invader, the uncontrolled growth of cancer, or mistimings in the contractions of individual heart cells.”
Swain also describes the decision making of cells in terms of “flipping” from one state to another. The cell is not programmed to do only one thing but is capable of several different actions, depending on the circumstances or a whim of nature – e.g. producing more or less enzymes, or consuming more or less sugar.
But Swain is more concerned with higher things – not just the basic functions performed by the biochemical networks in cells (e.g. amplifiers and switches) but how the whole biochemical network is built – not so much the individual soldiers and the way that they fight but the generals, the battles and the whole war itself. In other words, Swain wants to understand how cells grow and evolve, as if they are consciously making strategic decisions, like people.
Evolution tends to make things more efficient by optimising a cell’s response to the environment, but this is often full of contradictions. Cells rarely operate alone but as part of a network or system of other cells, and an individual cell may ‘choose’ to cooperate with other cells so that all of them do well together, or it may decide to ‘cheat’ and pursue its own selfish agenda (e.g. consume a common resource such as sugar quickly but inefficiently rather than slowly and more efficiently), thus ensuring it wins at the expense of its ‘team-mates.’ In order to make its strategic decisions, via gene expression or biochemical processes, a cell appears to act as an intelligent being who processes data and makes statistical inferences, and this is where Swain has to think like a cell, not necessarily making the obvious choice, as he models the decision-making process and analyses probability in the quest to see how cells decide and respond.
“Ten years ago,” says Swain, “some scientists thought we should focus on breaking biochemical networks down into functional modules, which was very influential, but now I want to understand at a higher level and find the strategy implemented by the network to make decisions, and how that strategy is influenced by competition and cooperation with other cells. We’ve already established the presence of stochasticity, and so we expect that making reliable decisions is not easy for cells because they have to process stochastic signals with biochemistry that itself behaves stochastically.”
Cells do not always behave as expected, and this is where stochasticity comes into play. For example, if you treat bacteria with antibiotics, some of them may be resistant – say, one per cent. If you then treat the resistant bacteria (known as ‘persisters’) with the same antibiotics again, you would expect most if not all to survive once again, but only one per cent may be resistant, the same as the original sample. It’s like a football team made up entirely of goalkeepers (or resistant bacteria) – when it comes to match day (treatment with antibiotics), some will act as strikers and others will act as defenders as they find new positions to play in the game, or randomly ‘flip’ from one state to another.
Some biologists simply believe that stochasticity proves “variety is the spice of life.” Others are more intrigued by the overall strategy, and Swain is even starting to apply game theory to the whole process, using the same kind of concepts used during the Cold War, when the hawks and the doves tried to out-guess one another and brought the planet to the brink of destruction. “Game theory makes sense,” says Swain, “because organisms don’t operate alone – and they co-operate and cheat in a way similar to people. And sub-optimal behaviour in one context may be a good thing in another situation.”
When it comes to applications, Swain hopes that his work will contribute in a number of ways – for example, speeding up experiments and testing by building mathematical models which work out how therapeutic molecules target particular proteins, and helping to design drugs (like antibiotics) with a much better chance of success, because they take account of the strategies used by cells in the fight for survival. In Swain’s own words, his aim is to “quantitatively model stochasticity, find out how stochasticity has affected evolution and cellular design and then how we can exploit stochasticity for medicine and biotechnology.” Cancer and stem cell therapies may be among the first fields to benefit from his research, but the sky is the limit and the next step for Swain may be synthetic biology, where ideas such as building new circuits in cells may emerge in the very near future, putting engineering ideas into practice.
SULSA is a major step in this direction, and Swain is looking forward to forming new partnerships with fellow researchers and gaining access to the nationwide facilities available, to carry out experiments and test his latest theories. Already, he is talking to another university in Scotland with interests in synthetic biology, while closer to home, he is working with the Tyers Lab in Edinburgh on projects which focus on yeast. He also hopes to take advantage of the microfluidics devices being built by the Scottish Microelectronics Centre, also in Edinburgh, which would help to test the decisions made by individual cells.
By deciding to become part of SULSA, Swain is living proof of his own mathematical theories, but the beauty of stochasticity is that even he does not know where the
project will take him and how his own work will evolve, as he responds to the
environment around him.